The manufacture of high quality product on a repeated basis requires accurate control of the manufacturing process. The manufacturing of semiconductor wafers by the well-known planar process is no exception. Among the steps performed to manufacture a semiconductor wafer by the planar process is that of epitaxial layer growth, whereby one of more layers of atoms are grown on the wafer by way of chemical vapor deposition. With silicon semiconductor wafers, epitaxial layer growth is usually carried out by heating the wafer in a reactor while hydrogen and silicon tetrachloride are reacted, or alternatively, while silane is decomposed, to deposit silicon atoms on the wafer.
To obtain high quality semiconductor devices, the thickness of each layer of atoms grown on each of a batch of semiconductor wafers should be substantially uniform. In the past, layer thickness uniformity has been obtained, to the extent possible, by fabricating a sample batch of wafers and then measuring the layer thickness. Adjustments in the process are then made in accordance with differences between the measured layer thickness and a desired thickness value. The steps of: (1) fabricating a batch of a sample wafers, (2) measuring the layer thickness, and (3) adjusting the process parameters, are repeated a number of times until the difference between the measured thickness and the desired layer thickness is within a prescribed tolerance factor.
To obtain more precise control of various manufacturing processes, including the manufacture of semiconductor wafers, neural networks have been employed. Such networks are typically comprised of a plurality of simple computing elements, often referred to as neurons, a number of which function as simple, non-linear summing nodes. These neurons are connected together by links which each weight the signal received from an upstream node. The interconnected neurons attempt to mimic the human brain and thereby provide a mechanism that, in response to a set of inputs, yields one or more process-driving signals which, when applied to the process, cause the process to yield a desired output.
The above-described neural network is trained in accordance with past data. In other words, the weighting provided by the links connecting the nodes of the successive layers is established in accordance with the input to, and output of, the process. When properly trained, the neural network should function as an inverse model of the process. In response to a set of input signals representing a set of process output estimators, the artificial neural network should generate a set of process-driving signals, which, when applied to the process, cause it to produce a set of outputs that more closely approximates a desired set of outputs.
Present-day artificial neural networks are believed to suffer from the disadvantage that such networks are often not very robust with respect to the difference between the input/output relationship of the process estimator and the actual process itself. As a consequence of continuous deviations in the operating characteristics of the process, such artificial neural networks do not operate satisfactorily to achieve a desired state of process operation in a rapid manner.
Thus, there is a need for a technique for achieving feedback control of a process using a neural network which is not subject to the disadvantages of the prior art.